Visual loop-closing with image profiles

ABSTRACT

This paper investigates the ability of image profiles, pixel-intensity sums across subsets of a video stream, to support the crucial robotic skill of place recognition through visual information alone. Building from work in which image profiles are the fundamental image representation for a model of biological neural processing [3, 4, 5], this paper offers a conceptually simpler approach to simultaneous localization and mapping via a single camera (monocular SLAM). In contrast to feature-based approaches in which extraction and statistical post-processing dominate the computation, this work uses a representation suitable even for very simple autonomous platforms. Experiments demonstrate the ability of our profile-based path segments to compensate for the inevitable inaccuracies in odometry when creating consistent world maps.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.9 Robotics
          Subjects: Sensors

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding
            Subjects: Motion; Perceptual reasoning; Representations, data structures, and transforms

General Terms:
Design, Experimentation, Performance

Keywords:
simultaneous localization and mapping, vision-based spatial estimation, visual odometry